Abstract
Mobilization strategies are an essential part of political parties’ campaign communication. By mobilizing voters and supporters, parties promote civic participation in politics, the forms of which have multiplied given the possibilities of user activities on social media. To define their online mobilization strategies, parties have to choose which forms of participation (e.g., voting, donating, or liking or sharing a post) they will seek to mobilize. Understanding mobilization as a communicative appeal to engage audiences in participatory actions, in our study we conceptually linked parties’ mobilizing appeals with three campaign functions—information, interaction, and mobilization—to systematize different types of mobilization. We applied that categorization to the social media campaigns of parties and top candidates in Germany and conducted a manual quantitative content analysis of 1,495 Facebook and 1,088 Instagram posts published in the run‐up to the 2021 federal election. Results show that parties primarily mobilized their audiences to vote and seek out more information (e.g., on the party’s website). Although user reactions are generally an important factor of performance on social media, parties mostly avoided calls to like, share, or comment on posts. When compared, the strategies of parties and candidates indicate that mobilization is more the task of parties than of candidates. Differences between Facebook and Instagram can be attributed to the different technical affordances of the platforms. Because Facebook, unlike Instagram, supports clickable links in posts, parties are more likely to encourage users on Facebook to seek out more information online.
Dokumententyp: | Zeitschriftenartikel |
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Fakultät: | Sozialwissenschaften > Kommunikationswissenschaft |
Themengebiete: | 300 Sozialwissenschaften > 300 Sozialwissenschaft, Soziologie
300 Sozialwissenschaften > 380 Handel, Kommunikation, Verkehr |
URN: | urn:nbn:de:bvb:19-epub-107783-8 |
ISSN: | 2183-2439 |
Sprache: | Englisch |
Dokumenten ID: | 107783 |
Datum der Veröffentlichung auf Open Access LMU: | 17. Nov. 2023, 13:00 |
Letzte Änderungen: | 20. Jun. 2024, 07:00 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892 |